A bayesian model for Diffusion Tensorial Magnetic Resonance Images denosing and reconstruction is considered. This is based on a Tikhonov like-functional for Total Generalized Variation and Rician likelihood which is described in a variational framework. A primal-dual algorithm is implemented and accurate numerical solutions of the associated saddle-point formulation are computed. An automatic parameter selection rule is proposed to facilitate practical clinical usage and diagnostic of neurodegenerative disorders. © 2013 Springer-Verlag.
CITATION STYLE
Martín, A., & Schiavi, E. (2013). Automatic total generalized variation-based DTI Rician denoising. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7950 LNCS, pp. 581–588). https://doi.org/10.1007/978-3-642-39094-4_66
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